Cargando…
Artificial intelligence and machine learning in emergency medicine: a narrative review
AIM: The emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine. METHODS: We un...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8887797/ https://www.ncbi.nlm.nih.gov/pubmed/35251669 http://dx.doi.org/10.1002/ams2.740 |
_version_ | 1784660981271494656 |
---|---|
author | Mueller, Brianna Kinoshita, Takahiro Peebles, Alexander Graber, Mark A. Lee, Sangil |
author_facet | Mueller, Brianna Kinoshita, Takahiro Peebles, Alexander Graber, Mark A. Lee, Sangil |
author_sort | Mueller, Brianna |
collection | PubMed |
description | AIM: The emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine. METHODS: We undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments. RESULTS: This review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI. CONCLUSION: We intend that this review serves as an introduction to AI and machine learning in emergency medicine. |
format | Online Article Text |
id | pubmed-8887797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88877972022-03-04 Artificial intelligence and machine learning in emergency medicine: a narrative review Mueller, Brianna Kinoshita, Takahiro Peebles, Alexander Graber, Mark A. Lee, Sangil Acute Med Surg Reviews AIM: The emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine. METHODS: We undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments. RESULTS: This review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI. CONCLUSION: We intend that this review serves as an introduction to AI and machine learning in emergency medicine. John Wiley and Sons Inc. 2022-03-01 /pmc/articles/PMC8887797/ /pubmed/35251669 http://dx.doi.org/10.1002/ams2.740 Text en © 2022 The Authors. Acute Medicine & Surgery published by John Wiley & Sons Australia, Ltd on behalf of Japanese Association for Acute Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Reviews Mueller, Brianna Kinoshita, Takahiro Peebles, Alexander Graber, Mark A. Lee, Sangil Artificial intelligence and machine learning in emergency medicine: a narrative review |
title | Artificial intelligence and machine learning in emergency medicine: a narrative review |
title_full | Artificial intelligence and machine learning in emergency medicine: a narrative review |
title_fullStr | Artificial intelligence and machine learning in emergency medicine: a narrative review |
title_full_unstemmed | Artificial intelligence and machine learning in emergency medicine: a narrative review |
title_short | Artificial intelligence and machine learning in emergency medicine: a narrative review |
title_sort | artificial intelligence and machine learning in emergency medicine: a narrative review |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8887797/ https://www.ncbi.nlm.nih.gov/pubmed/35251669 http://dx.doi.org/10.1002/ams2.740 |
work_keys_str_mv | AT muellerbrianna artificialintelligenceandmachinelearninginemergencymedicineanarrativereview AT kinoshitatakahiro artificialintelligenceandmachinelearninginemergencymedicineanarrativereview AT peeblesalexander artificialintelligenceandmachinelearninginemergencymedicineanarrativereview AT grabermarka artificialintelligenceandmachinelearninginemergencymedicineanarrativereview AT leesangil artificialintelligenceandmachinelearninginemergencymedicineanarrativereview |